Inspection and measurement of steel mass is critical in many applications. For example, corrosion in pipelines can lead to degradation of pipe strength and possible ruptures. In infrastructure applications, corrosion in steel can lead to reduction in the load-bearing capability that these structures can handle. This can lead to collapse of buildings made with reinforced steel or bridge failures as pre-stressed tendons give away.
Detection of steel mass is especially important in segmental bridges where the bridge's load-bearing ability depends on the steel of pre-stressed tendons. Tendons, in this case, hold the numerous segments of a segmental bridge together. The loading capability of a bridge depends on the number of tendons linking the segments together and the health of steel inside each tendon. A tendon typically fails when steel strands snap; thus increasing the load on other tendons holding the bridge together. If enough tendons snap, the remaining tendons cannot hold the bridge weight together and a bridge collapse is imminent. Another risk factor for tendons is in the form of steel corrosion. Here, the load-bearing capability is weakened when the steel strands inside the tendons corrode. It is critical to continuously inspect these bridge components for corrosion in the tendons and to detect for failed or broken tendons. Corrosion is brought on by air and moisture in contact with steel, as well as by high chloride content within the grouting or fill material surrounding the steel.
Current inspection methods depend on either inaccurate or inefficient methods. For example, magnetic flux is based on generating magnetic field at one side of the tendon and detecting the strength of that field as it is received from a detector end. This method, being the most viable solution that currently exists, has two major drawbacks. First, it is labor and time intensive as it requires repeatedly winding and unwinding heavy cables. Second, it is not very accurate as the magnetic field detected at the receiver end is typically very low. Another method is based on the use of microwave signals transmitted into the structure. A receiver in this case detects the electromagnetic waves as they bounce back from the steel rods. However, this method also suffers from two major drawbacks: First, the steel rods are typically located relatively near to the surface where the sensors are mounted. This makes deciphering the received signals from multiple reflections difficult; and thus accuracy is reduced. Second, this method is more suited to detecting if steel rods are present or not; it cannot accurately detect corrosion of steel mass.
In the present invention, a new system and method is used to overcome the disadvantages in currently existing methods. The new invention uses a magnetic source and pressure sensors mounted against a surface. Based on the mass of steel inside a volume, the magnetic source is attracted to the surface and exerts pressure on the pressure sensor that is proportional to the mass of steel being detected. An electronic device reads the pressure value and uses the information to quantify the steel inside the volume.
A further extension of this invention involves the mounting of several magnetic-pressure sensors around the volume being detected. The collective pressure signals from the sensors are used to map the location of steel inside the volume being tested.
The present invention can be applied wherever there is a need to measure the mass of steel in a non-invasive manner. In addition to infrastructure Non-Destructive Testing (NDT), inspection of pipes for detection of corrosion based on the available thickness of steel is also made possible.
The present invention relates to a system and process to obtain a relationship between pressure exerted on pressure sensors and the area (or volume) of steel in the imaging domain. In one embodiment, multiple sensors are mounted around cylindrical objects like tendons or pipes. In another embodiment, the magnetic-pressure sensor modules are placed on plane or flat surfaces.
Electrical Capacitance Volume Tomography (ECVT) is a non-invasive imaging modality. Its applications span an array of industries. Most notably, ECVT is applicable to multiphase flow applications commonly employed in many industrial processes. ECVT is often the technology of choice due to its advantages of high imaging speed, scalability to different process vessels, flexibility, and safety. In ECVT, sensor plates are distributed around the circumference of the column, object or vessel under interrogation. The number of sensor plates may be increased to acquire more capacitance data. However, increasing the number of sensor plates reduces the area of each sensor plate accordingly. A limit exists on the minimum area of a sensor plate for a given column diameter, thus limiting the maximum number of plates that can be used in an ECVT sensor. This limit is dictated by the minimum signal-to-noise ratio requirement of the data acquisition system. Since ECVT technology is based on recording changes in capacitance measurements induced by changes in dielectric distribution (i.e., phase distribution), and the capacitance level of a particular sensor plate combination is directly proportional to the area of the plates, minimum signal levels are needed to provide sufficiently accurate measurements. These considerations dictate the required minimum sensor plate dimensions. This limitation on the minimum size of the sensor plates, while increasing the number of available sensor plates in an ECVT sensor, is one of the main hurdles in achieving a high resolution imaging system.
To overcome this challenge, the concept of Adaptive Electrical Capacitance Volume Tomography (AECVT) was recently developed, whereby the number of independent capacitance measurements is increased through the use of reconfigurable synthetic sensor plates composed of many smaller sensor plates (constitutive segments). These synthetic sensor plates maintain the minimum area for a given signal-to-noise ratio (SNR) and acquisition speed requirements while allowing for many different combinations of (synthetic) sensor plates in forming a sensor plate pair.
Electrical Capacitance Tomography (ECT) is the reconstruction of material concentrations of dielectric physical properties in the imaging domain by inversion of capacitance data from a capacitance sensor. Electrical Capacitance Volume Tomography or ECVT is the direct 3D reconstruction of volume concentrations or physical properties in the imaging domain utilizing 3D features in the ECVT sensor design. An ECVT system is generally made up of a sensor, sensor electronics and a computer system for reconstruction of the image sensed by the sensor. An ECVT sensor is generally comprised of n electrodes or plates placed around a region of interest, in one embodiment providing n(n−1)/2 independent mutual capacitance measurements which are used for image reconstruction. Image reconstruction is performed by collecting capacitance data from the electrodes placed around the wall outside the vessel. ECVT technology is described in U.S. Pat. No. 8,614,707 to Warsito et al. which is hereby incorporated by reference.
Adaptive Electrical Capacitance Volume Tomography (AECVT) provides higher resolution volume imaging of capacitance sensors based on different levels of activation levels on sensor plate segments. In AECVT systems, electrodes are comprised of an array of smaller capacitance segments that may be individually addressed. For example, each segment may be activated with different amplitudes, phase shifts, or frequency to provide the desired sensitivity matrix distribution. The sensor electronics of the present invention is designed to detect and measure the capacitance for the adaptive ECVT sensor of the present invention. For example, the difference in electrical energy stored in the adaptive ECVT sensor would be measured between an empty state and a state where an object is introduced into the imaging domain (e.g., between the electrodes). In a preferred embodiment of the invention, the term “adaptive” means the ability to provide selective or high resolution control through the application of voltage or voltage distributions to a plate having an array of capacitance segments. The change in overall energy of the system due to the introduction of a dielectric material in the imaging domain is used to calculate the change in capacitance related to the dielectric material. The change in capacitance can be calculated from the change in stored energy. Sensor electronics can also be designed by placing individual segment circuits in parallel yielding a summation of currents representing total capacitance between segments under interrogation. By individually addressing the capacitance segments of the electrodes of the present invention, electric field distribution inside the imaging domain can be controlled to provide the desired sensitivity matrix, focus the electric field, and increase overall resolution of reconstructed images. Voltage distribution can also be achieved by using a conventional measuring circuit with a sensor that distributes voltages through a voltage divider.
In AECVT systems, a capacitance measurement circuit is connected to an electrode (detecting or receiving electrode) of the adaptive sensor so that a capacitance measurement can be obtained for the selected source and detecting electrodes. The capacitors Cx1-Cxn of the sensor represent the n number of capacitance segments of the selected source electrode and the detecting electrode. Each capacitance segment of the electrodes can be individually addressed by separated voltage sources. These voltage sources are used for regulating the voltage levels and phase shifts on the capacitance segments of each of the electrodes on the adaptive sensor. The voltage across each of the capacitor segments (Vxn) is the combination of the voltage source Vi and the voltage sources connected to each capacitor segment (Vn). Accordingly, the measured Vo can be used to calculate each of the equivalent capacitance (Cxn) of the capacitance segments of the activated electrode. The associated formula is for Cxn=Cx1=Cx2 . . . =Cxi. For segments with different capacitance values, the equivalent capacitance is calculated using the formula:
      V    0    =            (                        j          ⁢                                          ⁢          ω          ⁢                                          ⁢                      R            f                                    1          +                      j            ⁢                                                  ⁢            ω            ⁢                                                  ⁢                          C              f                        ⁢                          R              f                                          )        ⁢          (                        Σ                      i            =            1                    n                ⁢                  V          xi                ⁢                  C          xi                    )      
As discussed, in one embodiment, n(n−1)/2 independent mutual capacitance measurements are measured and used for image reconstruction. For example, the capacitance between each of the electrodes of the sensor are measured in turn and image reconstruction is performed using this capacitance data. In other words, capacitance measurements are obtained from every pair or electrode combination of the sensor, in turn, to be used in image reconstruction. It is appreciated that the voltage sources herein discussed may be connected to the capacitance segments of each of the electrodes of the sensor array using known switch technologies. Using switches, the system can selectively choose which electrodes to activate by connecting the voltage sources to the selected electrodes through the switches. In another embodiment, switching or multiplexing circuit elements can be used to connect the appropriate voltage sources to each of the capacitance segments of the selected electrode allowing various elements to be selectively connected to each capacitance segment depending on the focus and sensitivity desired. For example, voltage sources of greater amplitude may be switched or connected to the capacitance segments in the center of the electrode or imaging domain so as to focus the measurements towards the center of the electrode or imaging domain.
In an alternate embodiment, instead of using different amplitudes, different frequencies may be used to activate electrode segments enabling concurrent measurements of different capacitance values introduced by electric field beams of different frequencies. In yet another alternate embodiment, different phase shifts may be used to activate electrode segments enabling steering of the electric field inside the imaging domain. The measured change in output voltage can be used to calculate the change in capacitance levels between the capacitance segments which are then used to reconstruct volume images of objects or materials between the sensors. AECVT is described in U.S. Pat. No. 9,259,168 to Marashdeh et al. which is hereby incorporated by reference.
In ECT, ECVT, or AECVT, the capacitance measurement between sensor plates is also related to the effective dielectric content between that plate pair. The SART method can be extended to all measurements of ECT, ECVT, or AECVT sensors, thus providing a high resolution visual representation of each phase through image reconstruction. These previous ECVT systems incorporate data acquisition system that increase imaging resolution through sensing capacitances from 3D conventional and adaptive capacitance sensors. Data acquisition systems are also described in U.S. patent application Ser. No. 14/191,574 (Publication No. US-2014-0365152-A1) which is hereby incorporated by reference.
Electrical capacitance sensors are used for non-invasive imaging by distributing the electric field inside the imaging domain in 3D. ECVT sensors enable sensitivity variation in the imaging domain that can utilize different plate shapes and distributions to target a volume for imaging.